Problem-based learning in pre-clinical medical education: 22 years of outcome research
Bibliographic record
Abstract
PURPOSE: To conduct a systematic review of problem-based learning (PBL) in undergraduate, pre-clinical medical education. METHODS: A research librarian developed comprehensive search strategies for MEDLINE, PSYCINFO, and ERIC (1985-2007). Two reviewers independently screened search results and applied inclusion criteria. Studies were included if they had a comparison group and reported primary data for evaluative outcomes. One reviewer extracted data and a second reviewer checked data for accuracy. Two reviewers independently assessed methodological quality. Quantitative synthesis was not performed due to heterogeneity. A qualitative review with detailed evidence tables is provided. RESULTS: Thirty unique studies were included. Knowledge acquisition measured by exam scores was the most frequent outcome reported; 12 of 15 studies found no significant differences. Individual studies demonstrated either improved clerkship (N = 3) or residency (N = 1) performance, or benefits on some clinical competencies during internships for PBL (N = 1). Three of four studies found some benefits for PBL when evaluating diagnostic accuracy. Three studies found few differences of clinical (or practical) importance on the impact of PBL on practicing physicians. CONCLUSIONS: Twenty-two years of research shows that PBL does not impact knowledge acquisition; evidence for other outcomes does not provide unequivocal support for enhanced learning. Work is needed to determine the most appropriate outcome measures to capture and quantify the effects of PBL. General conclusions are limited by methodological weaknesses and heterogeneity across studies. The critical appraisal of previous studies, conducted as part of this review, provides direction for future research in this area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.083 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".